Featured Application: Neural Network Structure Learning is expected to overcome difficulty of constructing a neural network structure.Abstract: This paper proposes the variable chromosome genetic algorithm (VCGA) for structure learning in neural networks. Currently, the structural parameters of neural networks, i.e., number of neurons, coupling relations, number of layers, etc., have mostly been designed on the basis of heuristic knowledge of an artificial intelligence (AI) expert. To overcome this limitation, in this study evolutionary approach (EA) has been utilized to automatically generate the proper artificial neural network (ANN) structures. VCGA has a new genetic operation called a chromosome attachment. By applying the VCGA, the initial ANN structures can be flexibly evolved toward the proper structure. The case study applied to the typical exclusive or (XOR) problem shows the feasibility of our methodology. Our approach is differentiated with others in that it uses a variable chromosome in the genetic algorithm. It makes a neural network structure vary naturally, both constructively and destructively. It has been shown that the XOR problem is successfully optimized using a VCGA with a chromosome attachment to learn the structure of neural networks. Research on the structure learning of more complex problems is the topic of our future research.
This article presents a hierarchical and modular traffic simulation environment for intelligent transportation systems (ITS). A conventional traffic simulation model is classified as microscopic or macroscopic; however, abstraction-level, vehicle-level, cell-level, road-level, and street-level traffic models can be coherently integrated and developed. Such an abstraction process is important for flexible traffic analysis since it reduces the complexity of a model, retaining its validity relative to the modeling objectives and experimental condition. To do this, the authors have proposed the four-layered approach: (1) system entity structure/model base, (2) model abstraction, (3) traffic modeling, and (4) ITS simulation systems layer. A proposed methodology has been successfully applied for building the advanced traveler information system and the advanced traffic management system. The abstraction method of a road network in traffic modeling and simulation will be widely used because it is possible to express and analyze various requirements of the traffic analyst hierarchically and structurally.
The phenomenon that the state of software degrades with time is known as software aging. The primary method to fight aging is software rejuvenation. This paper presents new ways of effective software rejuvenation using virtualization for addressing software aging. This new approach is meant to be the less disruptive as possible for the running service and to get a zero downtime in most of the cases. We construct the state transition models to describe the behaviors of virtualized and nonvirtualized application server. We map through the rejuvenation actions to this transition model with stochastic process and express availability, downtime and downtime costs in terms of the parameters in our models. Our results show that virtualization and software rejuvenation can be used to prolong the availability of the services.
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